4 papers in the archive (2024), average empirical rigor 2.6/10, average math complexity 8.9/10. Quadrant mix: Holy Grail ×1, Lab Rats ×3.
Works mostly in Options & Derivatives Pricing, Risk Management & Tail Risk, Stochastic Control & Optimal Stopping, Insurance & Actuarial Risk.
Frequent co-authors: Ella Elazkany (2), Martin Schweizer (1), Michèle Vanmaele (1), Mich`ele Vanmaele (1), Emmanuel Lepinette (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
This paper explores the application and significance of the second-order Esscher pricing model in option pricing and risk management. We split the study into two main parts. First, we focus on the constant jump diffusion (CJD) case, analyzing the behavior of option prices as a function of the second
In this paper, we consider the discrete-time setting, and the market model described by (S,F,T)$. Herein F is the ``public" flow of information which is available to all agents overtime, S is the discounted price process of d-tradable assets, and T is an arbitrary random time whose occurrence might
In this paper, we introduce the second-order Esscher pricing notion for continuous-time models. Depending whether the stock price $S$ or its logarithm is the main driving noise/shock in the Esscher definition, we obtained two classes of second-order Esscher densities called linear class and exponent
The classic stochastic Fubini theorem says that if one stochastically integrates with respect to a semimartingale $S$ an $η(dz)$-mixture of $z$-parametrized integrands $ψ^z$, the result is just the $η(dz)$-mixture of the individual $z$-parametrized stochastic integrals $\intψ^z{d}S.$ But if one want